Rise and Fall of the DHSI Twitterati?

NOTE: This is a lightly edited version of a talk I gave at the inaugural Chesapeake DH 2020 conference. My original intention was to wait to publish this until after June, to see if the trends I’d identified continued through this year’s DHSI. But the disruptions of the COVID-19 pandemic and the subsequent cancellation of pretty much everything have created enough of a rupture in the dataset that I’ve decided it is not advisable to extend my analysis past 2019.

TL;DR – people have wondered if DHSI Twitter is dying. I hypothesize, based on my analysis, that it is not dying so much as democratizing. First, we saw an explosion of Tweets as the network expanded to encompass more than a clique of early adopters (2014-6), then we saw a contraction as many Twitterati moved on or moved into the instructor corps (2017-9), leaving more “space” in the network for everyone else.

Rise and Fall of the DHSI Twitterati? A Longitudinal Analysis of the Digital Humanities Summer Institute Twitter Hashtags from 2012-2019

Digital humanists have been using Twitter to share their experiences at the annual Digital Humanities Summer Institute since at least 2009 and Twitter has become a staple of the DHSI experience, with official hashtags, organizer accounts, and a variety of prizes awarded to prolific or entertaining tweeters.  But beginning in 2018 and again in 2019, people noted the decreasing volume of DHSI tweets, with one user speculating this might be “finally the turn away from twitter“.  While it’s a bit soon to toll the death knell of DHSI Twitter—800 people wrote over 5000 tweets on the #dhsi19 hashtag—the absolute numbers do seem to tell a simple tale of an increasing number of people tweeting in the early part of the decade, which peaked in 2015 and 2016 and has been in decline ever since.  However, these absolute numbers are a mask for series of more complicated patterns.  This talk looks at DHSI Twitter from 2012 through 2019, examining the changing institutional circumstances of the institute, the expansion and fragmentation of the initial tweeting clique, and the role of power-tweeters (or “Twitterati”) in developing and sustaining DHSI Twitter.

I’m going to skip any detailed discussion of method and instead point you to my Twitter Methods Ur-Post if you’d like to read about data collection and processing. While I collected most of this data myself, I’d also like to give a shoutout to Jon Martin for collecting and sharing the first years in this dataset.

graph of 2017 DHSI tweets per day showing spikes during the two working weeks of DHSI

While I’ve collected Tweets over varying lengths of time, you can see from this graph the number of total DHSI tweets in 2017 that there is a sharp increase in number of tweets before the event and an equally sharp decline after the event, which made me comfortable cutting off long tail of tweets before and after DHSI without fear of missing too much.

close up of 2017 DHSI tweets per week, demonstrating Mondays as highest tweet days, falling a bit on Tuesday, Wednesday, Thursday, a spike on Friday, then a dramatic decrease into the weekends

This is a visualization of +/- 2 days for 2017 so can see boundaries more clearly. In general, there is a pattern of a sharp uptick in the number of tweets in the first three days of each week – as people are traveling, then excited to be at DHSI – followed by a decrease over the next two days – as people are getting stuff done – with a bit of an uptick on the last day – as people give one last hurrah and declare the week was fun. There’s a sharp decrease over the weekend between the two weeks of DHSI, and an equally steep drop off at end of the entire event. Because of this, I feel comfortable focusing on the primary dates of each year’s DHSI, plus or minus 1 travel day (e.g. for 2017 this would be days 155 to 168 in the visualization above).

graph of total people tweeting at DHSI, showing peak in 2015-6 and slow decline afterwards

With those preliminaries out of the way, on to the fun stuff! If you look at the total number of people Tweeting at DHSI, concerns about decrease in Twitter activity seem correct if perhaps a little overblown. There’s a steady decline in 2017, 2018, with sharp drop off in 2019, less than any year since 2014, but who knows what 2020 would have looked like under other circumstances.

graph showing tweets per week at DHSI, showing going from 1 to 3 weeks in 2015 and 3 to 2 weeks in 2016, with tweets per week falling dramatically in 2015, rising again to 2017, then falling again after

If we look at total number of Tweets, we get the same tale but perhaps more starkly. There is a peak in 2015, fewer Tweets in 2016, a slight recovery in 2017, and a sharp drop off in 2018 and 2019 which take us back down to the level of 2012. The sharp drop in 2018 all the more remarkable for the fact that total number of Tweeters was relatively steady from 2017 to 2018 – there were 20 fewer people but almost 5000 fewer Tweets!

But if we look at second bar in this chart – which is Tweets per week – we see the first complication in this simple narrative. In this column, the sharpest drop-off was actually between 2014 – the last year DHSI was 1 week only – and 2015 – when DHSI attempted to expand to 3 weeks in a grueling marathon. While this might logically have been expected to produce a threefold increase in Tweets, attendees report severe burnout and the institute was scaled back down to 2 weeks for all subsequent years.  Indeed, burnout remains an issue and attendees are warned against it annually.

The total tweets per week recovered a bit in 2016 and 2017, but never reached heights of 2014.  By that standard, DHSI Twitter has been dying since 2014.

graph of number of Twitterati tweeting 100+, 100-199, and 200+ tweets over time, showing spikes in all three categories around 2015 and a spike in 200+ tweets in 2017

The second complication emerges when we look at a subset of the DHSI Tweeters, who I like to call Power-Tweeters or the Twitterati: people who Tweeted more than 100 times during any year of DHSI. To reach the status of Twitterati, a person has to produce 20+ Tweets a day if they only attend 5 days, or 9+ Tweets a day if they attend 2 weeks as well as the weekend in between. I’ve mapped here all the Twitterati, as well as subdivided the group into people with 200+ Tweets and people with 100-199 Tweets. (Note that, while 200 is the minimum number of Tweets to be in the first group, some people in that group are Tweeting 400, 600, even 817 times in a single DHSI.)

There aren’t many people who made it to 200 tweets when DHSI was only 1 week and we see the number of Twitterati in this category double in 2015, when the event went to 3 weeks followed by a decline in 2016 when the event was scaled back to 2 weeks.  The number of Twitterati in the 100-199 saw its biggest increase a bit earlier, in 2014, and its biggest decrease also in 2016.  But the number of Twitterati in this group have actually been holding steady since 2017, with even a slight increase into 2019.

This is in sharp contrast to the 200+ Twitterati. There was a spike in their numbers during 2017 followed by a huge drop off to 2018.  In 2017, the twelve 200+ Twitterati were responsible for 3741 Tweets, but in 2018 the three 200+ Twitterati barely managed 781.  In other words, 3000 of the 5000 Tweet difference between 2017 and 2018 can be accounted for by the decreased activity of this group.  The trend continued into 2019, when only one person who managed to make it over 200 Tweets, and they barely squeaked over that line at 216.

So when it comes to the forces driving the overall number of DHSI Tweets, it seems clear that what we’re seeing is a drop in the number of Twitterati.  While there’s also been a drop in the overall number of Tweeters, that seems more closely correlated to the drop in attendance at DHSI from 2018 to 2019.

chart of DHSI tweets over time reading: year # weeks # participant-weeks # tweeters # tweets avg # of tweets/week # ppl w/200+ tweets # ppl w/100+ tweets # of “power-tweeters” associated event 2012 1 388 393 4790 4790 1 6 7 - 2013 1 433 470 6786 6787 4 8 12 - 2014 1 558 954 11250 11250 5 19 24 - 2015 3 719 1248 13672 4557.3 11 19 30 - 2016 2 743 1258 10854 5427 8 13 21 ELO/ INKE 2017 2 833 1109 12146 6073 12 9 21 SHARP 2018 2 879 1089 7158 3579 3 9 12 DLF/ SINM 2019 2 790 808 5450 2725 1 10 11 ADHO SIG Pedagogy

For those who are interested in the numbers, here’s the chart where you can can see how Twitterati correlate to number of Tweets and the sharp drop off in both for 2018 and 2019. It also shows the changing overall attendance numbers for DHSI. (Note that this column counts people twice if attended both weeks and uses public registration lists to determine attendance so it is not 100% accurate but close enough for our purposes.)

In this chart, you can also see a third possible complication that might be driving some of these numbers, which is the weekend conference.  While the ADHO Sig Pedagogy and DLF events were great, those were also offshoots of a scholarly conference that happens at other times of year (the international DH conference and the DLF Forum, respectively).  By contrast, 2017 was the year that SHARP held its one and only conference in conjunction with DHSI.  And SHARP is another event that has historically been filled with Tweeters, with Twitter prizes and attendees who were paid to live-Tweet the conference in multiple languages.  So a lot of what is driving the anomalous numbers in 2017 can actually be pinned on SHARP and SHARP’s Twitterati.

force-directed network graph that is too big for anyone to get much information out of

At this point, we’ve seen a few complications to our original, simple narrative, and hopefully I’ve intrigued you about the role of Twitterati at DHSI. Now it’s time to move onto… the super-network!  Here is a visualization of the entire DHSI Twitter network from 2012-2019. It has 6539 nodes and 69594 edges (the red edges are 2012, orange is 2013, through to purple which are 2019 edges) in 43 connected components, of which 98.1% of nodes and 99.84% of edges are in the giant connected component.

close up view of force-directed network layout, still too difficult for most people to read but with a few Twitterati names popping out for those with sharp eyes

And because network visualizations like this are always spaghetti monsters, here’s a slightly closer view, with nodes sized by degree – the number of times the person with that Twitter handle Tweeted or was Tweeted to/about – and colored by modularity class – algorithmically generated subnetworks. From here, you can already begin to see some of the Twitterati’s handles pop out, such as dorothyk98, profwernimont, DHInstitute, and AlyssaA_DHSI. I’m hanging out in the upper left hand corner. But this visualization also begins to let you see the sheer number of people who’ve attended and tweeted at DHSI over the years and how little the network has shifted over time. That is, there are no clear subnetworks, where each year emerges as an independent cluster of nodes.

small multiples of a force-directed network graph showing the overlap of people attending each year of DHSI

You can see that a bit easier here, with each year separated out in the visualization. If you look closely, you can see there has been some drift over time:

  • the red 2012 nodes are mostly to the bottom left of the combined visualization
  • the pink 2013 nodes are to bottom right with some 2012 overlap
  • the orange 2014 nodes are in the center and low bottom with significant 2013 overlap
  • the yellow 2015 nodes are in the center with significant 2014 overlap
  • the green 2016 nodes overlap most of 2015 and are a bit higher in the visualization
  • the teal 2017 nodes overlap 2015 and 2016, but are a bit off to the left/top
  • the dark blue 2018 nodes overlap 2017, but are a bit off to the right/top
  • the purple 2019 nodes overlap most of 2015-8 and are a bit lower in the center

This is consistent with many people coming to DHSI for several years (mostly in a row) then stopping when no longer need training. Alternatively, they move into the instructor pool and stop Tweeting as much or stop Tweeting altogether because they’re too busy instructing.

Separating them out also lets you see the year Twitter really “caught on” was between 2013 and 2014 – the numbers of Tweeters and connections doubled, while the density of network halved.  That is, this was no longer a tight clique of a few Tweeters but lots of people sharing the hashtag space.

chart: year # weeks # participant-weeks # nodes # edges connected components graph density avg. degree avg. path length associated event 2012 1 388 488 3623 4 .015 7.424 3.181 - 2013 1 433 647 5479 4 .013 8.468 3.185 - 2014 1 558 1244 11758 6 .008 9.452 3.292 - 2015 3 719 1603 13358 9 .005 8.333 3.281 - 2016 2 743 1629 11070 8 .004 6.796 3.404 ELO/ INKE 2017 2 833 1531 10573 23 .005 6.906 3.464 SHARP 2018 2 879 1309 7691 14 .004 5.875 3.537 DLF/ SINM 2019 2 790 1032 6042 16 .006 5.855 3.556 ADHO SIG Pedagogy

These are statistics on the network for each year’s DHSI. I know force-directed graphs are prettier than charts, but this chart lets us start to get into the meat of the network analysis, for example by showing you the changes I was just talking about between 2013 and 2014 more clearly.  Far from 2014 being the year DHSI Twitter started to die, from a network analysis standpoint 2014 is the year DHSI Twitter finally caught on.

Moving forward in time to examine the two years where DHSI’s format changed dramatically – going from 1 to 3 to 2 weeks – we see the slow evolution of the Twitter network and how it only partially reflected those changes. There was a slight increase in Tweets/connections from 2014 to 2015, but it wasn’t proportional given the move from 1 to 3 weeks.  We also see the graph density continue to decline, which makes sense: people attending only week 1 might not Tweet to those in week 2 or 3. The number of Tweeters increased from 2015 to 2016, and the number of overall connections decreased but again, it wasn’t proportional given the move from 3 to 2 weeks.

2017 stands out as an anomalous year in the network analysis as well. The number of Tweeters and connections decreased slightly from 2016 to 2017 but the number of connected components tripled. The average degree and density also increase a little, suggesting that SHARP attendees’ participation in the DHSI Twitter network both (slightly) increased the connectivity of its main connected component while at the same time fragmenting parts of it into far more numerous unconnected components.

In 2018, the number of Tweeters and connections both decreased markedly – as people were noticing on Twitter – but the number of Tweets actually declined at a much faster rate than number of connections. Again, this gives support to the hypothesis that these changers were driven by the Twitterati decreasing in numbers. People were still using Twitter to connect to each other, even if they weren’t producing as many tweets. Similarly, the number of Tweeters decreased markedly from 2018 to 2019 but the number of overall connections didn’t decrease in proportion to the decrease in the number of Tweets.

In other words, the DHSI Twitter network was still functioning as a network even though it seemed to have gone (relatively) radio silent.

force directed graph of DHSI Twitter without the Twitterati showing no large nodes but the vast majority of nodes/connections are still there

To consider the importance of the Twitterati another way, I first visualized the network without them. For the purposes of the DHSI “super network,” I defined the Twitterati more generously as anyone who has tweeted over 200 times at all DHSIs combined (so anyone who’s been at 3 or more DHSIs might get into the Twitterati even if weren’t in the Twitterati for any 1 year). This cuts out a mere 2% of the nodes but a whopping 75% of the edges.

Another way to think of this is to note that 36% of the DHSI Twitter network consists of nodes that are connected to it by 1 Tweet and another 17% are connected by 2 Tweets. In other words, the VAST majority of people connected to DHSI hashtags are the weakest of possible ties – 1 and done.  But these nodes share a quarter of the edges in the graph with each other and almost half the edges with the Twitterati.

force directed network graph of 123 Twitterati showing remarkable coherence over time

By contrast, the Twitterati themselves consist of 123 nodes.  That’s it. Only 2% of all nodes in the network have tweeted 200 or more times over this 8-year period, and amongst themselves they generate a bit under a third of all the edges.

force directed network of a few people who have tweeted over 1000 times at DHSI

So where does this leave us as I cram in the last few words before I’m out of time?  There is a very, very small number of people responsible for the vast majority of DHSI tweets, and if we zoom in to the 1000+ Tweets club, we can see how truly small a handful of people this is. There are 18 of them (well, us). Thus just a few Twitterati attending or not attending in a year (or, if you know who many of these pepole are, just a few Twitterati moving into the instructor corp!) can have an outsized impact on the network.

BUT ALSO, if you will allow me to return to an earlier visualization…

force directed graph of DHSI Twitter without the Twitterati showing no large nodes but the vast majority of nodes/connections are still there

…it’s possible what we have been seeing in 2018 and 2019 is not the demise of DHSI Twitter so much as the democratization of DHSI Twitter. Each year, there have been fewer and fewer Twitterati dominating the conversations, making space for everyone else. And that is as beautiful a network as the super-network I started with.  Thank you.

The Twitter Methods Ur-Post

For some years now, I’ve been analyzing conference Twitter data and sporadically posting about it online, including twitter threads written from various airports. While I’ve had a method from the beginning, that method has evolved over time: most significantly, I got tired of endless hours of Open Refine data cleaning and automated the network creation process. (To be clear: I love Open Refine. But when you have a tedious, repetitive task, programming is your friend.) While it’s possible that my methods will continue to evolve over time – as technology changes and new research questions occur to me – this is the current state of my methodology and will be linked as the ur-post whenever I blog about Twitter analysis.

Data Collection

I collect my data from Twitter using Hawksey’s TAGS 6.0, which employ Google Sheets. Yes, I know there’s now a TAGS 6.1 but I subscribe to the philosophy of “if it ain’t broke, don’t fix it.”

The primary advantage of TAGS, for me, is the ability to “set and forget.” TAGS utilizes the Google Search API (as opposed to the Google Streaming API) in its limited, free version, which means that it can only capture twitter data from the last 7 days. To get around this limitation, TAGS can be set up to query the API every hour and capture whatever new tweets occurred since the tweet “archive sheet” has last been updated. This means it can be set up whenever I remember to set it up – usually weeks if not months before a conference – and it will continue running until I remember to tell it to stop – again, usually weeks if not months after a conference ends.

I try to download this data regularly to my computer, according to the data management principle LOCKSS: Lots of Copies Keeps Stuff Safe. Only having the data available in Google Sheets makes me dependent on Google to get to my data. By contrast, CSV files on my computer, which is Time Machined in two locations, have a decent chance of surviving anything short of nuclear/zombie apocalypse.

Data Cleaning/Pre-Processing

While the data that I get from TAGS is relatively clean, I do tidy it up a bit first. Most importantly, I deduplicate my dataset. Some of this duplication is my fault, when I’m trying to track hashtag variants and someone includes both variants in a single tweet (e.g. “aha2019” and “aha19”). TAGS also seems to duplicate some of its collection data, though I haven’t figured out why – manual inspection of each tweet’s unique ID makes clear when a tweet is an actual duplicate in the set vs. a delete-and-rewrite or a retweet of someone else’s tweet. Because deduplication is a simple process of checking whether each tweet’s unique ID occurs only once in the dataset, I’ve automated that process.

Depending on the analysis I want to conduct, I also tend to time-limit my dataset. Specifically, I delete any tweets (from a copy of the spreadsheet – no one panic!) that occur more than one “day” before the start of the event or one “day” after the end of the event. In this case, a “day” is defined as the GMT day, which may or may not correspond to the local timezone of the event. While this has the potential to cause slight discrepancies when comparing events across timezones – specifically, some events will have a few more hours of data capture before the event starts while some will have a few more hours after it starts – I don’t believe these changes to be statistically significant. If I ever do some hard math on the question, I’ll update this post to indicate the results.

Network Creation

Now to the fun stuff! I started analyzing conference tweets because I was interested in how people connect and share knowledge/ideas/opinions in this virtual space. As such, my primary interest lies in creating a social network from the twitter data – who tweeted at/mentioned who – which necessitates transforming the TAGS archival spreadsheet of tweets into a network of Twitter handles (and/or hashtags). Because Twitter handles are unique IDs, I only needed an edge list of sources and targets for each tweet (other data capture was and remains interesting-but-optional).

I originally did this manually. Aside from being tedious, this also created problems for replicability. That is, what if I slipped up and missed or repeated something while creating my edge lists? I therefore wrote a Python script to do this work for me, with a few variants for if I wanted to keep the hashtag, date/time information, or for my students using TAGS 6.1.

Next I imported the edge list into Gephi (though I’ve experimented with other software, Gephi’s old hat to me at this point and does what I need it to do) and allow it to sum repeated edges to give each edge a weight. That is, if I tweeted to or re-tweeted @epistolarybrown 173 times over the course of a conference, the edge from me to her would have weight 173.

Network Analysis

At this point in the process, I use Gephi’s built-in algorithms to conduct my network analysis, usually with an emphasis on metrics like degree, betweenness centrality, network diameter/path lengths, and modularity classes. For an example of how that works out in practice, check out one of my conference blog posts! And if you have any questions, feel free to ping me via Twitter.


All of my code is available on Github under a MIT License.

Twitter at the Big Three: Global Network Stats

Every year in the break between Fall and Spring academic semesters, tens of thousands of scholars from across the world descend on an American city for several caffeine-fueled days of panels, receptions, job interviews, and social networking. Actually, this happens more than once, as members of the American Historical Association, the Modern Language Association, and the American Library Association all meet in January. And while most of their social networking happens face-to-face, some of it happens on Twitter where enterprising digital humanists armed with Martin Hawksey’s TAGS can collect conference tweets and analyze them for fun and profit.

Posts in this (intended) series include (and will be linked as they are published):

  1. Global Networks Stats
  2. Bipartite Network Analysis
  3. Directed Network Analysis
  4. Preliminary Conclusions (TL;DR)
  5. The Methods Post

So without further ado, here are some initial stats about the networks I constructed from the three official conference Twitter hashtags: #aha17, #mla17, and #alamw17.

The AHA network is the smallest at 2,826 nodes (people who either tweeted or whose twitter handle showed up in another person’s tweets) and 6,945 edges (connections generated by said tweets). These edges have been weighted so that if Person A mentions Person B 14 times in tweets, the edge from Person A to Person B has weight 14. If Person A mentions Person C only once, the edge from Person A to Person C has weight 1. The average degree is 2.5 (number of edges divided by number of nodes) but when weight is factored in (edges are multiplied by their weight before added and divided by number of nodes) the average weighted degree is 3.9.

There are 74 connected components (subnetworks with no connection to the rest of the network), with the largest connected component containing 90% of the nodes and 96% of the edges in the overall network. This component has diameter 10 (the shortest distance between two people furthest away from each other) and average path length 4.3 (average of the shortest distance between every pair of people in the network).

The MLA network is slightly bigger and slightly more connected:

  • nodes: 3,538
  • edges: 10,178
  • average degree: 2.9
  • average weighted degree: 5.2
  • connected components: 70
  • largest connected component contains
    • nodes: 94.2%
    • edges: 97.8%
  • diameter 12
  • average path length 4.4

The ALA network is the largest and most connected:

  • nodes: 7,851
  • edges: 20,505
  • average degree: 2.6
  • average weighted degree: 3.9
  • connected components: 99
  • largest connected component:
    • nodes: 96.1%
    • edges: 98.9%
  • diameter: 14
  • average path length: 5.4

So what happens when we put it all together?


Green edges = #aha17 hashtag. Red edges = #mla17 hashtag. Blue edges = #alamw17 hashtag.

Merging the three networks together creates some overlap of nodes (people on Twitter during more than one conference) and edges (people tweeting to the same people at more than one conference) but the three networks remain largely discrete. The force atlas 2 layout I employed in Gephi created more overlap of the AHA and MLA conferences than the ALA conference, but in general disciplinarity is the rule of the day.

While some of this is likely an artifact of most scholars’ inability to physically attend multiple conferences (the AHA and MLA, in particular, occurred at the same time in Colorado and Pennsylvania respectively), scholars have the ability to interact via Twitter with conferences they aren’t attending. The co-occurrance of the AHA and MLA could have – theoretically – increased connectivity between the two conferences if similar themes and conversations arose at both then connected via social media. Alas, I don’t have the 2015 metrics (the last time these conferences didn’t co-occur) to do a comparison, but if anyone has them and wants to share I’d love to see them!

In general, the merged “Big Three” network stats clearly derive from their constituent conferences’ stats:

  • nodes: 13,489
  • edges: 37,308
  • average degree: 2.8
  • average weighted degree: 4.5
  • connected components: 203
  • largest connected component:
    • nodes: 95%
    • edges: 98.3%
  • diameter: 16
  • average path length: 5.9

One of these numbers, however, immediately jumped out at me as not like the others: the number of connected components. If only the largest connected component of each conference network had been able to connected in the Big Three network, there should have been 74+(70-1)+(99-1)=241 connected components. Instead, 38 of the small components in the conference networks appear to have merged with another component (either the largest connected component or another small component).

This is encouraging to me as it implies that there is an interdisciplinary scholarly community that emerges on Twitter, not just in the dense “center” of the network but also in the disconnected “margins.” In is not (yet?) clear whether this interdisciplinary community is generated by digital humanists, librarians, geographical proximity, common interests, or – most likely – some combination of factors, including some I haven’t considered.

Regardless of the cause, something is going on. In the interests of exploring it, next time I’m going to restructure my data as a bipartite network to see if anything else interesting emerges.